Readability (Perplexity)
sonar 85.9% Dropped Models answer search questions. Graders rate whether answers are concise yet detailed and use markdown formatting well.
Perplexity reported it once, for Sonar, with no public method or data. The scoring method is not described, so model-judged is assumed.
Made by Perplexity Released Feb 2025 Taste and style Tasks kept private Last in a launch post Feb 2025 , dropped after it saturated
Last model added Feb 2025
- Level
- Everyday Most adults, without training
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Open-ended Free-form text, documents, designs or media judged as a whole.
1
Model scored in lab cards
1
Launch post lists it, from 1 lab
sonar leads with 85.9%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
- 1 sonar 85.9%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | sonar | 85.9% | Feb 2025 | Model card |
The best score passed 85% in Feb 2025
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Reported by the lab in its model card
Labs dropped it from launch posts once the best score passed 85%
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 11 Feb 2025 | sonar | 85.9 | Readability |
More in Writing and taste
- Design Bench (Moonshot) In use Models make graphic designs from direct prompts or few-shot reference images. An automated grader rates the results as Elo scores. Kimi K3.1 Pro 957
- WritingBench Fading Models complete 1,000 real-world writing requests across 6 domains and 100 subdomains. A judge model scores each reply on five query-specific criteria. GPT-5 83.9
- Creative Writing (MiniMax in-house) Only in system cards Models write creative pieces for real Hailuo AI user requests. Automatic assessments score the writing.
MiniMax-Text-01 81.3% - DeepSeek Chinese functional writing evaluation Only in system cards Models answer 3,170 everyday Chinese writing prompts such as reports, emails and speeches. Pairwise judging against a baseline model gives a win rate.
DeepSeek-V4-Pro 62.6% - Slide generation human evaluation (Z.ai) Only in system cards People compare HTML slide decks from two models on content, layout and visual quality. Metric is overall win rate. GLM-5 67.5%
- Task-dependent output homogenization evaluation Only in system cards Models answer prompts several times. A judge scores functional diversity where it is wanted and consistency where one answer is right.
Muse Spark 0.21